1. Technical Field
The present disclosure relates to an apparatus and method for a brain computer interface, and in particularly, to a technology for more quickly and accurately analyzing what a brain wave intends by measuring the brain wave.
2. Description of the Related Art
A brain computer interface (BCI) refers to means for connecting the brain of a creature with a computer to enable communications therebetween. The BCI directly connects the brain with the computer without conventional input/output devices using the creature's verbal or visual expressions. For example, it includes analyzing a brain wave generated during a brain activity in real-time so as to control a certain device.
The BCI technology may be used as a medical technology. When it is applied to a patient who lost a part of her/his body or whose body is paralyzed, it can enhance the satisfaction with life.
The BCI technology may use electrodes to learn what the brain wave means. Typically, to perform the BCI, a number of electrodes are installed into the brain of a subject commonly at different positions inside or outside the skull, and the BCI communicates with the subject by observing when and at which frequency a brain wave is detected from at least one of the electrodes.
However, the brain wave exhibits different results even under the same test conditions, depending on the location, time, mental state of the subject, hindrance, and the like. Herein, the metal state may include a thinking of abstract situations, a motor imagery (MI) (e.g., thinking of right hand action and thinking of left hand action), etc. If the test is carried out with another subject, the brain wave may exhibit more different results in the same test.
In order to accurately perform the BCI even though test conditions are changed due to these problems, a variety of filters have been introduced. For instance, “Optimal spatial filtering of single trial EEG during imagined hand movement” by Ramoser H, Muller-Gerking J and Pfurtscheller G, IEEE Trans. Rehabil. Eng., 2000 December; 8(4): 441-6; and “Optimizing Spatial filters for Robust EEG Single-Trial Analysis” by Blankertz B, Tomioka R, Lemm S, Kawanabe M and Muller K-R, IEEE Signal Process. Mag. 2008, vol. 25, 41-56 have been introduced. Such technologies have been introduced as a common spatial pattern (CSP) technology, which extracts brain wave features commonly detected from a number of spatially different brain waves, while the subject is thinking the same thing.
However, the above technologies have a problem that it takes long time to learn whenever the common brain wave is extracted due to the performance variations of brain signals over different sessions or subjects. Even trained CSP filters don't work in real-time and frequently yield biased results. In this case, the CSP filters must be retrained by new training data. Accordingly, they cannot be immediately applied to an emergency patient such as a quadriplegia patient and thus the patient must wait for a long time. In addition, the above technologies require a great amount of times for experiments to develop the BCI technology, and accordingly the BCI technology progresses too slowly.